Dzyaloshinskii–Moriya interaction gradient driven skyrmion based energy efficient leaky integrate fire neuron
The performance and energy efficiency of skyrmion-based spintronic devices can be greatly affected by the driving mechanism of skyrmion. Skyrmions in thin films can be efficiently displaced with spin-transfer torques (STT) and spin–orbit torques (SOT) using direct current. However, the utilization of current in these devices leads to the Joule heating issue, that compromises their non-volatility advantage in spintronic devices for neuromorphic computing. Therefore, it is necessary to investigate the alternative driving mechanisms for developing energy-efficient skyrmion based devices. In this work, skyrmion dynamics under Dzyaloshinskii–Moriya interaction (DMI) gradient is investigated. It is observed that the DMI gradient is an efficient way to drive the skyrmions with skyrmion Hall effect (SkHE) and a high longitudinal speed. Additionally, a leaky integrate-and-fire (LIF) neuron at room temperature is designed by employing DMI gradient that is energy efficient as it functions without the need for any current. This neuron utilizes a DMI gradient for integration on a trapezoidal-shaped nanotrack, achieving leaky functionality through the shape of the nanotrack itself. This design eliminates the need for external stimuli and additional hardware, resulting in low energy consumption of 91.363 fJ per spike. This opens a new alternative way to manipulate skyrmions for the development of energy-efficient skyrmion based devices.
Authors
- Brajesh Kumar Kaushik (ORCID: https://orcid.org/0000-0002-6414-0032)
- Shipra Saini (ORCID: https://orcid.org/0000-0002-8865-2956)
- Sonal Shreya (ORCID: https://orcid.org/0000-0001-6340-0368)
- Ravi Shankar Verma (ORCID: https://orcid.org/0000-0003-2045-8740)
- Ravish Kumar Raj (ORCID: https://orcid.org/0000-0001-5955-7354)
Institutions
- Indian Institute of Technology Roorkee (IN)
- Aarhus University (DK)
Publication Details
- Journal
- Journal of Magnetism and Magnetic Materials
- Published
- 2024-11-28
- DOI
- https://doi.org/10.1016/j.jmmm.2024.172694
- Citations
- 9
- Primary Topic
- Advanced Memory and Neural Computing
- Type
- article
- Field-Weighted Citation Impact
- 1.88
Funders
- European Commission
- Department of Science and Technology, Ministry of Science and Technology, India
- Council of Scientific and Industrial Research, India
- Ministry of Education
- Ministry of Education, India
- Science and Engineering Research Board